user
How can machine learning models be evaluated? Output:
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Machine learning models can be evaluated using performance metrics such as accuracy, precision, recall, and F1 score. Additionally, models can also be evaluated using insights from a confusion matrix, which provides information about false positives and false negatives and can help identify which types of errors a model is making. Furthermore, visualizing the results in a graph or chart can help to identify patterns and trends in the data. Finally, if the model is intended to solve a problem with real-world implications, such as medical diagnosis, it must also be evaluated by human experts to confirm that the model is safe and effective.